import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling


def 	lowerCamelCase					(	UpperCamelCase     :				List[str]			,       UpperCamelCase     :				Dict							)   ->    Any:
		_lowerCamelCase				   =  old_name

		if "patch_embed" in old_name:
				_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase				   =  old_name.split('.'							)

				if layer == "0":
						_lowerCamelCase				   =  old_name.replace('0'			,       'convolution1'							)
				elif layer == "1":
						_lowerCamelCase				   =  old_name.replace('1'			,       'batchnorm_before'							)
				elif layer == "3":
						_lowerCamelCase				   =  old_name.replace('3'			,       'convolution2'							)
				else:
						_lowerCamelCase				   =  old_name.replace('4'			,       'batchnorm_after'							)

		if "network" in old_name and re.search(R'\d\.\d'			,       UpperCamelCase							):
				_lowerCamelCase				   =  R'\b\d{2}\b'
				if bool(re.search(UpperCamelCase			,       UpperCamelCase							)							):
						_lowerCamelCase				   =  re.search(R'\d\.\d\d.'			,       UpperCamelCase							).group()
				else:
						_lowerCamelCase				   =  re.search(R'\d\.\d.'			,       UpperCamelCase							).group()
				if int(match[0]							) < 6:
						_lowerCamelCase				   =  old_name.replace(UpperCamelCase			,       ''							)
						_lowerCamelCase				   =  trimmed_name.replace('network'			,       match[0] + '.meta4D_layers.blocks.' + match[2:-1]							)
						_lowerCamelCase				   =  'intermediate_stages.' + trimmed_name
				else:
						_lowerCamelCase				   =  old_name.replace(UpperCamelCase			,       ''							)
						if int(match[2]							) < num_meta4D_last_stage:
								_lowerCamelCase				   =  trimmed_name.replace('network'			,       'meta4D_layers.blocks.' + match[2]							)
						else:
								_lowerCamelCase				   =  str(int(match[2]							) - num_meta4D_last_stage							)
								_lowerCamelCase				   =  trimmed_name.replace('network'			,       'meta3D_layers.blocks.' + layer_index							)
								if "norm1" in old_name:
										_lowerCamelCase				   =  trimmed_name.replace('norm1'			,       'layernorm1'							)
								elif "norm2" in old_name:
										_lowerCamelCase				   =  trimmed_name.replace('norm2'			,       'layernorm2'							)
								elif "fc1" in old_name:
										_lowerCamelCase				   =  trimmed_name.replace('fc1'			,       'linear_in'							)
								elif "fc2" in old_name:
										_lowerCamelCase				   =  trimmed_name.replace('fc2'			,       'linear_out'							)

						_lowerCamelCase				   =  'last_stage.' + trimmed_name

		elif "network" in old_name and re.search(R'.\d.'			,       UpperCamelCase							):
				_lowerCamelCase				   =  old_name.replace('network'			,       'intermediate_stages'							)

		if "fc" in new_name:
				_lowerCamelCase				   =  new_name.replace('fc'			,       'convolution'							)
		elif ("norm1" in new_name) and ("layernorm1" not in new_name):
				_lowerCamelCase				   =  new_name.replace('norm1'			,       'batchnorm_before'							)
		elif ("norm2" in new_name) and ("layernorm2" not in new_name):
				_lowerCamelCase				   =  new_name.replace('norm2'			,       'batchnorm_after'							)
		if "proj" in new_name:
				_lowerCamelCase				   =  new_name.replace('proj'			,       'projection'							)
		if "dist_head" in new_name:
				_lowerCamelCase				   =  new_name.replace('dist_head'			,       'distillation_classifier'							)
		elif "head" in new_name:
				_lowerCamelCase				   =  new_name.replace('head'			,       'classifier'							)
		elif "patch_embed" in new_name:
				_lowerCamelCase				   =  'efficientformer.' + new_name
		elif new_name == "norm.weight" or new_name == "norm.bias":
				_lowerCamelCase				   =  new_name.replace('norm'			,       'layernorm'							)
				_lowerCamelCase				   =  'efficientformer.' + new_name
		else:
				_lowerCamelCase				   =  'efficientformer.encoder.' + new_name

		return new_name


def 	lowerCamelCase					(	UpperCamelCase     :				Optional[int]			,       UpperCamelCase     :				Union[str, Any]							)   ->    List[str]:
		for key in checkpoint.copy().keys():
				_lowerCamelCase				   =  checkpoint.pop(UpperCamelCase							)
				_lowerCamelCase				   =  val

		return checkpoint


def 	lowerCamelCase					(	)   ->    List[str]:
		_lowerCamelCase				   =  'http://images.cocodataset.org/val2017/000000039769.jpg'
		_lowerCamelCase				   =  Image.open(requests.get(UpperCamelCase			,       stream=UpperCamelCase							).raw							)

		return image


def 	lowerCamelCase					(	UpperCamelCase     :				Path			,       UpperCamelCase     :				Path			,       UpperCamelCase     :				Path			,       UpperCamelCase     :				bool							)   ->    List[Any]:
		_lowerCamelCase				   =  torch.load(UpperCamelCase			,       map_location='cpu'							)['model']
		_lowerCamelCase				   =  EfficientFormerConfig.from_json_file(UpperCamelCase							)
		_lowerCamelCase				   =  EfficientFormerForImageClassificationWithTeacher(UpperCamelCase							)
		_lowerCamelCase				   =  '_'.join(checkpoint_path.split('/'							)[-1].split('.'							)[0].split('_'							)[:-1]							)

		_lowerCamelCase				   =  config.depths[-1] - config.num_metaad_blocks + 1
		_lowerCamelCase				   =  convert_torch_checkpoint(UpperCamelCase			,       UpperCamelCase							)

		model.load_state_dict(UpperCamelCase							)
		model.eval()

		_lowerCamelCase				   =  {
		    'bilinear': PILImageResampling.BILINEAR,
		    'bicubic': PILImageResampling.BICUBIC,
		    'nearest': PILImageResampling.NEAREST,
		}

		# prepare image
		_lowerCamelCase				   =  prepare_img()
		_lowerCamelCase				   =  2_56
		_lowerCamelCase				   =  2_24
		_lowerCamelCase				   =  EfficientFormerImageProcessor(
		    size={'shortest_edge': image_size}			,       crop_size={'height': crop_size, 'width': crop_size}			,       resample=pillow_resamplings['bicubic']			,       )
		_lowerCamelCase				   =  processor(images=UpperCamelCase			,       return_tensors='pt'							).pixel_values

		# original processing pipeline
		_lowerCamelCase				   =  Compose(
		    [
		        Resize(UpperCamelCase			,       interpolation=pillow_resamplings['bicubic']							),
		        CenterCrop(UpperCamelCase							),
		        ToTensor(),
		        Normalize(UpperCamelCase			,       UpperCamelCase							),
		    ]							)
		_lowerCamelCase				   =  image_transforms(UpperCamelCase							).unsqueeze(0							)

		assert torch.allclose(UpperCamelCase			,       UpperCamelCase							)

		_lowerCamelCase				   =  model(UpperCamelCase							)
		_lowerCamelCase				   =  outputs.logits

		_lowerCamelCase				   =  (1, 10_00)

		if "l1" in model_name:
				_lowerCamelCase				   =  torch.Tensor(
				    [-0.1_312, 0.4_353, -1.0_499, -0.5_124, 0.4_183, -0.6_793, -1.3_777, -0.0_893, -0.7_358, -2.4_328]							)
				assert torch.allclose(logits[0, :10]			,       UpperCamelCase			,       atol=1e-3							)
				assert logits.shape == expected_shape
		elif "l3" in model_name:
				_lowerCamelCase				   =  torch.Tensor(
				    [-1.3_150, -1.5_456, -1.2_556, -0.8_496, -0.7_127, -0.7_897, -0.9_728, -0.3_052, 0.3_751, -0.3_127]							)
				assert torch.allclose(logits[0, :10]			,       UpperCamelCase			,       atol=1e-3							)
				assert logits.shape == expected_shape
		elif "l7" in model_name:
				_lowerCamelCase				   =  torch.Tensor(
				    [-1.0_283, -1.4_131, -0.5_644, -1.3_115, -0.5_785, -1.2_049, -0.7_528, 0.1_992, -0.3_822, -0.0_878]							)
				assert logits.shape == expected_shape
		else:
				raise ValueError(
				    F"""Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"""							)

		# Save Checkpoints
		Path(UpperCamelCase							).mkdir(exist_ok=UpperCamelCase							)
		model.save_pretrained(UpperCamelCase							)
		print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""							)
		processor.save_pretrained(UpperCamelCase							)
		print(F"""Processor successfuly saved at {pytorch_dump_path}"""							)

		if push_to_hub:
				print('Pushing model to the hub...'							)

				model.push_to_hub(
				    repo_id=F"""Bearnardd/{pytorch_dump_path}"""			,       commit_message='Add model'			,       use_temp_dir=UpperCamelCase			,       )
				processor.push_to_hub(
				    repo_id=F"""Bearnardd/{pytorch_dump_path}"""			,       commit_message='Add image processor'			,       use_temp_dir=UpperCamelCase			,       )


if __name__ == "__main__":
				A						=			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--pytorch_model_path',
				    default=None,
				    type=str,
				    required=True,
				    help='Path to EfficientFormer pytorch checkpoint.',
				)
				parser.add_argument(
				    '--config_file',
				    default=None,
				    type=str,
				    required=True,
				    help='The json file for EfficientFormer model config.',
				)
				parser.add_argument(
				    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
				)

				parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
				parser.add_argument(
				    '--no-push_to_hub',
				    dest='push_to_hub',
				    action='store_false',
				    help='Do not push model and image processor to the hub',
				)
				parser.set_defaults(push_to_hub=True)

				A						=			parser.parse_args()
				convert_efficientformer_checkpoint(
				    checkpoint_path=args.pytorch_model_path,
				    efficientformer_config_file=args.config_file,
				    pytorch_dump_path=args.pytorch_dump_path,
				    push_to_hub=args.push_to_hub,
				)
from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm


def 	lowerCamelCase					(	UpperCamelCase     :				np.ndarray			,       UpperCamelCase     :				np.ndarray							)   ->    float:
		return math.sqrt(sum(pow(a - b			,       2							) for a, b in zip(UpperCamelCase			,       UpperCamelCase							)							)							)


def 	lowerCamelCase					(	UpperCamelCase     :				np.ndarray			,       UpperCamelCase     :				np.ndarray							)   ->    list[list[list[float] | float]]:

		if dataset.ndim != value_array.ndim:
				_lowerCamelCase				   =  (
				    'Wrong input data\'s dimensions... '
				    F"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
				)
				raise ValueError(UpperCamelCase							)

		try:
				if dataset.shape[1] != value_array.shape[1]:
						_lowerCamelCase				   =  (
						    'Wrong input data\'s shape... '
						    F"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
						)
						raise ValueError(UpperCamelCase							)
		except IndexError:
				if dataset.ndim != value_array.ndim:
						raise TypeError('Wrong shape'							)

		if dataset.dtype != value_array.dtype:
				_lowerCamelCase				   =  (
				    'Input data have different datatype... '
				    F"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
				)
				raise TypeError(UpperCamelCase							)

		_lowerCamelCase				   =  []

		for value in value_array:
				_lowerCamelCase				   =  euclidean(UpperCamelCase			,       dataset[0]							)
				_lowerCamelCase				   =  dataset[0].tolist()

				for dataset_value in dataset[1:]:
						_lowerCamelCase				   =  euclidean(UpperCamelCase			,       UpperCamelCase							)

						if dist > temp_dist:
								_lowerCamelCase				   =  temp_dist
								_lowerCamelCase				   =  dataset_value.tolist()

				answer.append([vector, dist]							)

		return answer


def 	lowerCamelCase					(	UpperCamelCase     :				np.ndarray			,       UpperCamelCase     :				np.ndarray							)   ->    float:
		return np.dot(UpperCamelCase			,       UpperCamelCase							) / (norm(UpperCamelCase							) * norm(UpperCamelCase							))


if __name__ == "__main__":
				import doctest

				doctest.testmod()